Abstract
Head pose estimation is a crucial step for numerous face applications such as gaze tracking and face recognition. In this paper, we introduce a new method to learn the mapping between a set of features and the corresponding head pose. It combines a filter based feature selection and a Generalized Regression Neural Network where inputs are sequentially selected through a boosting process. We propose the Fuzzy Functional Criterion, a new filter used to select relevant features. At each step, features are evaluated using weights on examples computed using the error produced by the neural network at the previous step. This boosting strategy helps to focus on hard examples and selects a set of complementary features. Results are compared with three state-of-the-art methods on the Pointing 04 database.
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Bailly, K., Milgram, M., Phothisane, P. (2009). Head Pose Estimation by a Stepwise Nonlinear Regression. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_3
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DOI: https://doi.org/10.1007/978-3-642-03767-2_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03766-5
Online ISBN: 978-3-642-03767-2
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